Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A computer-implemented method, comprising: obtaining, by a computer processor, electric field data corresponding to settings of a non-cylindrically symmetrical implanted leadwire that is adapted for stimulating anatomical tissue, the electric field data including for each of a plurality of neural elements a respective plurality of electric values for a same electric field parameter; determining in a first determining step, by the processor and for each of the neural elements, a respective activation status based on the respective plurality of electric values associated with the respective neural element; and the processor determining in a second determining step, and outputting an indication of, an estimated activated tissue region corresponding to a combination of points surrounding the leadwire corresponding to those of the neural elements for which an active status is determined; wherein the determining of the second determining step is performed by executing a first module that at least one of: does not base the determining of the second determining step on input of different sets of values of an electric field at different points in time; does not use more than one differential equation; or is generated based on observed functioning of a second module that uses differential equations, wherein the first module uses only linear equations.
A computer system estimates the activation of anatomical tissue around a non-cylindrical implanted lead wire. It receives electric field data (multiple electric field values for each neural element, like axons) at different points around the lead wire. For each neural element, it determines if it's activated based on these electric field values. It then identifies the estimated activated tissue region around the lead wire by combining the locations of the activated neural elements. This calculation is performed by a simplified model (module) that either avoids time-dependent electric field data, uses only one differential equation, or is a linear equation model trained to mimic a more complex differential equation model.
2. The method of claim 1 , wherein the first module is automatically generated by execution of a machine learning module based on analysis by the machine learning module of functioning of the second module.
The system described in claim 1 uses a simplified model to estimate the activated tissue. This model is automatically generated by a machine learning system. The machine learning system analyzes the behavior of a more complex model that uses differential equations and then creates the simplified model based on that analysis. This makes the process of generating the simplified model automatic and efficient.
3. The method of claim 2 , wherein the machine learning module is an Artificial Neural Network learning module.
The machine learning system mentioned in claim 2, which automatically creates the simplified model, is an Artificial Neural Network (ANN). The ANN learns from the complex model's behavior and creates the simplified model based on this learning.
4. The method of claim 1 , wherein the neural elements are axons.
In the system described in claim 1, the neural elements being analyzed to determine activation are axons, the long, slender projections of nerve cells that conduct electrical impulses.
5. The method of claim 4 , wherein the axons are all copies of a same model axon, each plotted at a respective point surrounding the leadwire.
Referring to claim 4, the axons are copies of the same model axon, each placed at different locations surrounding the lead wire. This allows the system to simulate how the electric field from the lead wire affects different axons in the surrounding tissue.
6. The method of claim 1 , wherein the electric field parameter is a second difference activating function.
In the system described in claim 1, the electric field parameter used to determine activation status is a "second difference activating function," a measure related to the change in the electric field gradient.
7. The method of claim 6 , further comprising: selecting, by the processor, for each of the plurality of neural elements, and as the respective plurality of electric values for the neural element, the second difference activating function values located at predefined locations of the respective neural element.
Building upon claim 6, for each neural element, the system selects the second difference activating function values at specific, predefined locations on that neural element. These values are then used to determine the activation status of that neural element.
8. The method of claim 7 , wherein the predefined locations are nodes of Ranviér of the respective neural element.
Regarding claim 7, the predefined locations where the second difference activating function values are taken are the nodes of Ranvier, which are gaps in the myelin sheath of the axon where ion channels are concentrated.
9. A computer-implemented method, comprising: responsive to receipt of user input settings of an implanted leadwire that is adapted for stimulating anatomical tissue, wherein the implanted leadwire is non-cylindrically symmetrical: determining in a first determining step, by a computer processor, electric field data corresponding to the user input settings, the electric field data including for each of a plurality of neural elements a respective plurality of electric values for a same electric field parameter; determining in a second determining step, by the processor and for each of the neural elements, a respective activation status based on the respective plurality of electric values associated with the respective neural element; determining in a third determining step, by the processor, an estimated activated tissue region corresponding to a combination of points surrounding the leadwire corresponding to those of the neural elements for which an active status is determined; and generating and displaying, by the processor, a graphical representation of a volume relative to at least one of a graphical representation of the leadwire and a graphical representation of anatomical structures, the volume corresponding to, and being based on, the estimated activated tissue region, wherein one or more of the determining in the second determining step or the determining in the third determining step is performed by executing a first module that at least one of: does not base the one or more of the determining in the second determining step or the determining in the third determining step on input of different sets of values of an electric field at different points in time: does not use more than one differential equation; or is generated based on observed functioning of a second module that uses differential equations, wherein the first module uses only linear equations.
This system displays a graphical representation of the stimulated tissue volume around a non-cylindrical implanted leadwire based on user-defined settings. It receives user input settings for the leadwire, determines the electric field data, and calculates the activation status of neural elements. The system then estimates the activated tissue region and displays it as a volume relative to the leadwire and anatomical structures. One or more steps in determining the activation status or activated tissue region rely on a simplified model (module) that either avoids time-dependent electric field data, uses only one differential equation, or is a linear equation model trained to mimic a more complex differential equation model.
10. A computer-implemented method, comprising: obtaining, by a computer processor executing a machine learning module, output data of a first module that determines an activation status for each of a plurality of neural elements based on input characterizing stimulation settings of one or more non-cylindrically symmetrical implanted leadwires adapted for stimulating anatomical tissue; obtaining, by the processor executing the machine learning module, at least a portion of the input processed by the first module to produce the obtained output data; analyzing, by the processor executing the machine learning module, the obtained output data and input; and based on the analysis, automatically generating, by the processor executing the machine learning module, at least one second module that determines an activation status for each of a plurality of neural elements based on input that (a) is different than the input and (b) characterizes stimulation settings of one or more non-cylindrically symmetrical implanted leadwires adapted for stimulating anatomical tissue.
A computer system automatically generates a simplified model for predicting neural activation near implanted lead wires. A machine learning module takes the output of a complex model (which calculates neural activation based on lead wire stimulation settings). It also takes a portion of the input used by the complex model. The machine learning module analyzes this input and output data. Based on this analysis, it automatically generates a simplified model to determine the activation status of neural elements, using *different* input that still characterizes the stimulation settings of the non-cylindrical lead wires.
11. The method of claim 10 , wherein the machine learning module is an Artificial Neural Network (ANN) module.
The machine learning module described in claim 10 for generating the simplified model is an Artificial Neural Network (ANN).
12. The method of claim 11 , wherein the ANN module implements pattern recognition with back-propagation of errors.
The Artificial Neural Network (ANN) module in claim 11 uses pattern recognition with back-propagation of errors to learn and improve the simplified model.
13. The method of claim 10 , wherein the machine learning module implements at least one of decision tree analysis, association rules, genetic algorithms, and support vector machines.
The machine learning module in claim 10 uses techniques like decision tree analysis, association rules, genetic algorithms, or support vector machines to generate the simplified model.
14. The method of claim 10 , wherein the machine learning module implements supervised learning based on user input rules.
The machine learning module in claim 10 uses supervised learning based on rules provided by a user to generate the simplified model.
15. The method of claim 10 , wherein the input includes for the each of the plurality of neurons a respective activating function profile.
The input to the machine learning module, as discussed in claim 10, includes an activating function profile for each neural element. This profile contains information about how the element responds to electrical stimulation.
16. The method of claim 15 , wherein the activating function profile includes a plurality of values of second central different activating function, each of the values being at a respective one of a plurality of predetermined positions of the respective neural element.
Building on claim 15, the activating function profile contains values of the second central difference activating function measured at specific, predetermined positions on the neural element.
17. The method of claim 16 , wherein the neural elements are axons and the plurality of predetermined positions are nodes of Ranviér.
Referring to claim 16, if the neural elements are axons, these predetermined positions are the nodes of Ranvier.
18. The method of claim 10 , wherein each of the plurality of neural elements is one of an axon, a dendrite, a cell body, and a glial cell.
The neural elements considered in claim 10 can be axons, dendrites, cell bodies, or glial cells.
19. The method of claim 10 , wherein the input includes at least one of a potential field, an electric tensor field, and a current tensor field.
The input mentioned in claim 10 includes a potential field, an electric tensor field, or a current tensor field, representing the electrical environment around the leadwire.
20. The method of claim 10 , wherein the at least one second module includes a plurality of second modules, each adapted for performing the determining of the activation status for the each of the plurality of neural elements for a respective pulse width category.
The simplified model generated in claim 10 can consist of multiple sub-models, each designed for a specific pulse width category of the electrical stimulation.
21. The method of claim 10 , wherein the at least one second module includes a first version module for determining activation statuses for neural elements associated with cathodically shaped activation function profiles, a second version module for determining activation statuses for neural elements associated with anodically shaped activation function profiles, and a third version module for determining activation statuses for neural elements associated with mixed anodic and cathodic activation function profiles.
The simplified model in claim 10 can include separate versions for handling different shapes of activation function profiles: one for cathodically shaped profiles, one for anodically shaped profiles, and one for mixed anodic/cathodic profiles.
22. The method of claim 10 , wherein the one or more implanted leadwires for which the one or more second modules are adapted to determine activation statuses are different than the one or more implanted leadwires for the output data of the first module was provided.
The simplified model generated in claim 10 is adapted for lead wires that are *different* from the lead wires used to generate the output data of the initial, complex model.
23. The method of claim 10 , further comprising: providing, by the processor executing the machine learning nodule, a same input to each of the first and second modules; comparing, by the processor executing the machine learning module, to each other respective output produced by each of the first and second modules in response to the same input; and responsive to determining that the compared output differ, modifying, by the processor executing, the machine learning module, the second module.
The system in claim 10 feeds the same input to both the complex model and the simplified model, compares their outputs, and modifies the simplified model if the outputs differ, improving its accuracy.
24. A computer-implemented method, comprising: obtaining, by a computer processor, electric field data corresponding to settings of non-cylindrically symmetrical implanted leadwire that is adapted for stimulating anatomical tissue, the electric field data including for each of a plurality of neural elements a respective plurality of electric values for a same electric field parameter; and determining and outputting, by the processor and for each of the neural elements, a respective activation threshold based on the respective plurality of electric values associated with the respective neural element; wherein the determining is performed by executing a first module that at least one of: does not base the determining on input of different sets of values of an electric field at different points in time; does not use more than one differential equation; or is generated based on observed functioning of a second module that uses differential equations, wherein the first module uses only linear equations.
A computer system calculates the activation threshold for neural elements surrounding a non-cylindrical implanted lead wire. It receives electric field data for each neural element (multiple electric field values for the same electric field parameter). It then determines and outputs each neural element's activation threshold based on these values. This calculation relies on a simplified model (module) that either avoids time-dependent electric field data, uses only one differential equation, or is a linear equation model trained to mimic a more complex differential equation model.
25. A computer-implemented method, comprising: obtaining, by a computer processor executing a machine learning module, output data of a first module that determines an activation threshold for each of a plurality of neural elements based on input characterizing stimulation settings of one or more non-cylindrically symmetrical implanted leadwires adapted for stimulating anatomical tissue; obtaining, by the processor executing the machine learning module, at least a portion of the input processed by the first module to produce the obtained output; analyzing, by the processor executing the machine learning module, the obtained output data and input; and based on the analysis, automatically generating, by the processor executing the machine learning module, at least one second module that determines an activation threshold for each of a plurality of neural elements based on input that (a) is different than the input and (b) characterizes stimulation settings of one or more non-cylindrically symmetrical implanted leadwires adapted for stimulating anatomical tissue.
A computer system automatically generates a simplified model for predicting neural activation thresholds near implanted lead wires. A machine learning module takes the output of a complex model (which calculates neural activation thresholds based on lead wire stimulation settings). It also takes a portion of the input used by the complex model. The machine learning module analyzes this input and output data. Based on this analysis, it automatically generates a simplified model to determine the activation threshold of neural elements, using *different* input that still characterizes the stimulation settings of the non-cylindrical lead wires.
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October 17, 2017
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